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Journal articles on the topic 'Data-Driven Strategy'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Matai, Puneet. "The Data - Driven Advantage - Crafting an Effective Enterprise Data Strategy for Financial Institutions." International Journal of Science and Research (IJSR) 10, no. 6 (2021): 1846–48. http://dx.doi.org/10.21275/sr24731194633.

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El Aissi, Mohamed El Mehdi, Sarah Benjelloun, Younes Lakhrissi, and Safae El Haj Ben Ali. "Big Data Enabling Fish Farming Data-Driven Strategy." Ingénierie des systèmes d information 27, no. 06 (2022): 949–56. http://dx.doi.org/10.18280/isi.270611.

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Tian, Entong, Jifu Guan, Chuanyao Sun, Liuheng Gu, and Yifei Zhang. "A data‐driven chassis coordination control strategy." IET Intelligent Transport Systems 15, no. 8 (2021): 1006–17. http://dx.doi.org/10.1049/itr2.12069.

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Jihua, Sun, and Shi Lin. "Research on Data-driven Marketing Strategy Optimization." Journal of Business and Marketing 1, no. 2 (2024): 157–61. http://dx.doi.org/10.62517/jbm.202409224.

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With the rapid development of information technology and the advent of the big data era, data-driven marketing strategy optimization has gradually become an important means for enterprises to improve market competitiveness and efficiency. This study aims to explore how to optimize marketing strategies through data-driven methods to achieve market precision and personalization. Firstly, the study reviews the theoretical foundation and related research achievements of data-driven marketing strategy optimization. Secondly, through the collection and analysis of market data, machine learning and d

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Kumar, S. "Relevance driven strategy for clinical data collection." Indian Journal of Urology 12, no. 2 (1996): 93. http://dx.doi.org/10.4103/0970-1591.21689.

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Wang, Bingxing. "Big Data-Driven ESG Quantitative Investment Strategy." Journal of Economic Theory and Business Management 2, no. 2 (2025): 8–13. https://doi.org/10.70393/6a6574626d.323837.

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With sustainability becoming more important worldwide, investors are looking more closely at environmental, social, and governance (ESG) factors. This paper looks at how big data could help investors use ESG information effectively in quantitative investing. It discusses how the use of big data techniques can lead to more accurate and transparent ESG analyses. Using regression models, the study identifies a positive relationship between companies' ESG scores and their expected stock returns. It also illustrates how detailed big data analysis can enrich the evaluation of corporate ESG performan

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Benjelloun, Sarah, Mohamed El Mehdi El Aissi, Younes Lakhrissi, and Safae El Haj Ben Ali. "Data Lake Architecture for Smart Fish Farming Data-Driven Strategy." Applied System Innovation 6, no. 1 (2023): 8. http://dx.doi.org/10.3390/asi6010008.

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Thanks to continuously evolving data management solutions, data-driven strategies are considered the main success factor in many domains. These strategies consider data as the backbone, allowing advanced data analytics. However, in the agricultural field, and especially in fish farming, data-driven strategies have yet to be widely adopted. This research paper aims to demystify the situation of the fish farming domain in general by shedding light on big data generated in fish farms. The purpose is to propose a dedicated data lake functional architecture and extend it to a technical architecture

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Zhou, Dan. "Tourism destination marketing strategy driven by big data." Journal of Computational Methods in Sciences and Engineering 24, no. 4-5 (2024): 2593–609. http://dx.doi.org/10.3233/jcm-247486.

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In this study, the marketing strategy of tourism destination driven by big data is deeply discussed. Firstly, the application of big data in the tourism industry and the current strategy of tourism destination marketing are analyzed, and then the research methods are designed through factor analysis and big data analysis theory. After processing and factor analysis of the collected data, the influence of big data on tourism destination marketing strategy is analyzed, and the possible optimization path is explored. At the same time, the problems and challenges encountered in this process are al

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Williams, Peter, and Amir Peleg. "Shifting Toward a Predictive, Data-Driven Operations Strategy." Journal - American Water Works Association 111, no. 2 (2019): 22–27. http://dx.doi.org/10.1002/awwa.1232.

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Piartrini, Putu Saroyini, PA Andiena Nindya Putri, Djoko Widagdo, Ansir Launtu, and Sauri Sauri. "Data-Driven Marketing Strategy to Reach Millennial Consumers." Dinasti International Journal of Education Management And Social Science 5, no. 4 (2024): 420–28. https://doi.org/10.38035/dijemss.v5i4.2452.

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This article explores the efficacy of data-driven marketing strategies in targeting millennial consumers, a demographic known for its unique preferences and digital savvy. Through a comprehensive literature review, various aspects of data-driven marketing, including customer segmentation, personalized content creation, and omnichannel integration, are analyzed in the context of reaching and engaging with millennials. The review encompasses studies on consumer behavior, digital marketing trends, and data analytics techniques relevant to understanding millennial preferences and behavior. Key fin

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Tian, Jiwei, Buhong Wang, and Xia Li. "Data-Driven and Low-Sparsity False Data Injection Attacks in Smart Grid." Security and Communication Networks 2018 (September 26, 2018): 1–11. http://dx.doi.org/10.1155/2018/8045909.

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Recent researches on data-driven and low-sparsity data injection attacks have been presented, respectively. To combine the two main goals (data-driven and low-sparsity) of research, this paper presents a data-driven and low-sparsity false data injection attack strategy. The proposed attacking strategy (EID: Eliminate-Infer-Determine) is divided into three stages. In the first step, the intercepted data is preprocessed by sparse optimization techniques to eliminate the outliers. The recovered data is then exploited to learn about the system matrix based on the parallel factorization algorithm i

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12

Lee, Kuan-Cheng, Hong-Tzer Yang, and Wenjun Tang. "Data-driven online interactive bidding strategy for demand response." Applied Energy 319 (August 2022): 119082. http://dx.doi.org/10.1016/j.apenergy.2022.119082.

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Zhang, Liyan, Min Zhang, Jian Ma, and Jing Ge. "A Data-Driven Control Strategy for Urban Express Ramp." Computational Intelligence and Neuroscience 2021 (November 16, 2021): 1–12. http://dx.doi.org/10.1155/2021/6540972.

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Expressway, as the main artery of urban traffic, realizes the smooth operation of the whole urban road network through reasonably balancing the traffic flow. However, due to the lack of reasonable and effective traffic control, the safety and congestion of expressways are becoming more and more serious. The development of intelligent network technology provides a new idea to solve the control problem of expressways. In this paper, a data-driven ramp control model of urban expressway is constructed. The interaction of traffic information is realized through intelligent network connection techno

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Hong, Shengyang. "Data-driven precision marketing strategy and its effect measurement." Transactions on Economics, Business and Management Research 12 (September 28, 2024): 59–64. http://dx.doi.org/10.62051/d63mm017.

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In today's rapidly developing digital age, data-driven precision marketing strategies have become a powerful tool for enterprises to stand out and gain key competitive advantages in fierce market competition. The beginning of this article elaborates in detail on the significant importance of data-driven precision marketing. In the current era of information explosion, through comprehensive collection, in-depth analysis, and deep mining of massive data, enterprises can gain a comprehensive and multi-dimensional understanding of consumers' specific needs, behavior patterns, and unique preference

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Xu, Zhiwei, Tianhu Deng, Zechun Hu, Yonghua Song, and Jianhui Wang. "Data-Driven Pricing Strategy for Demand-Side Resource Aggregators." IEEE Transactions on Smart Grid 9, no. 1 (2018): 57–66. http://dx.doi.org/10.1109/tsg.2016.2544939.

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Tong, Loong Cheong. "A data-driven control strategy for grammar writing systems." Machine Translation 4, no. 4 (1989): 281–97. http://dx.doi.org/10.1007/bf00713702.

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Patel, R., T. Pham, C. Love, et al. "DATA-DRIVEN STRATEGY FOR OPTIMIZING CRYOPRESERVED PRODUCT INVENTORY MANAGEMENT." Cytotherapy 26, no. 6 (2024): S120. http://dx.doi.org/10.1016/j.jcyt.2024.03.227.

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18

张, 王俊. "Research on Security Driven Edge Cloud Data Collaboration Strategy." Computer Science and Application 13, no. 11 (2023): 2062–71. http://dx.doi.org/10.12677/csa.2023.1311205.

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Teutsch, Johannes, Sebastian Ellmaier, Sebastian Kerz, Dirk Wollherr, and Marion Leibold. "An Online Adaptation Strategy for Direct Data-driven Control." IFAC-PapersOnLine 56, no. 2 (2023): 644–49. http://dx.doi.org/10.1016/j.ifacol.2023.10.1640.

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Yuan, Zhu, Xueqiang Lv, Yunchao Gong, Boshan Liu, Haixiang Yang, and Xindong You. "Text Semantics-Driven Data Classification Storage Optimization." Applied Sciences 14, no. 3 (2024): 1159. http://dx.doi.org/10.3390/app14031159.

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Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices to orchestrate a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. The traditional strategy of solely relying on access frequency for data classification is no longer suitable for today’s complex storage environment. Diverging from conventional methods, we explore from the perspective of text semantics to address this issue and propose an effective data classification storage method using text semantic similarity to ext

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Salvador, Jose Ramon, Ramirez Daniel R., Teodoro Alamo, and de la Peña David Muñoz. "Offset free data driven control: Application to a process control trainer." IET Control Theory and Applications 13, no. 18 (2019): 3096–106. https://doi.org/10.1049/iet-cta.2019.0376.

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This work presents a data driven control strategy able to track a set point without steady-state error. The control sequence is computed as an affine combination of past control signals, which belong to a set of trajectories stored in a process historian database. This affine combination is computed so that the variance of the tracking error is minimised. It is shown that offset free control, that is zero mean tracking error, is achieved under the assumption that the state is measurable, the underlying dynamics are linear and the trajectories of the database share the same error dynamics and a

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KOSTETSKY, A. N., and D. P. KAYFEDZHAN. "PROBLEMS AND POSSIBILITIES OF USING THE DATA-DRIVEN APPROACH IN BUILDING MEDIA STRATEGIES OF LOGISTICS COMPANIES." Economics: Theory and Practice 77, no. 1 (2025): 78–82. https://doi.org/10.31429/2224042x_2025_77_78.

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The article discusses the possibilities of applying the data-driven approach, which is relevant in the context of marketing digitalization, when building media strategies for logistics companies. The authors analyzed the trends in the development of the market of marketing technologies (MarTech) as prerequisites for the emergence of a data-driven approach in marketing. Based on the analysis of the problems and prospects of using data marketing tools, the factors influencing the construction of a media strategy reflecting the specifics of the promotion of logistics services are highlighted.

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23

Xu, Wenjuan. "The strategy of user experience design driven by big data." Journal of Physics: Conference Series 1774, no. 1 (2021): 012042. http://dx.doi.org/10.1088/1742-6596/1774/1/012042.

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24

He, Bingjie, Qiaorong Dai, Aijuan Zhou, and Jinxiu Xiao. "Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing." Journal of Mathematics 2021 (October 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/7477314.

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Applying the optimal problem, we get the optimal power supply and price. However, how to make the real power consumption close to the optimal power supply is still worth studying. This paper proposes a novel data-driven inverse proportional function-based repeated-feedback adjustment strategy to control the users’ real power consumption. With the repeated-feedback adjustment, we adjust the real-time prices according to changes in the power discrepancy between the optimal power supply and the users’ real power consumption. If and only if the power discrepancy deviates the preset range, the real

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25

Li, Chaojie, Chen Liu, Ke Deng, Xinghuo Yu, and Tingwen Huang. "Data-Driven Charging Strategy of PEVs Under Transformer Aging Risk." IEEE Transactions on Control Systems Technology 26, no. 4 (2018): 1386–99. http://dx.doi.org/10.1109/tcst.2017.2713321.

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26

Metcalf, R. A., M. B. Pagano, J. R. Hess, J. Reyes, J. D. Perkins, and M. I. Montenovo. "A data-driven patient blood management strategy in liver transplantation." Vox Sanguinis 113, no. 5 (2018): 421–29. http://dx.doi.org/10.1111/vox.12650.

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27

谢, 淋东. "The Optimization Strategy of Data Driven Urban Rail Transit Network." Advances in Applied Mathematics 09, no. 06 (2020): 919–24. http://dx.doi.org/10.12677/aam.2020.96109.

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28

Zheng, Wei, Chao Wang, Yajun Yang, and Yongfei Zhang. "Multi-objective combustion optimization based on data-driven hybrid strategy." Energy 191 (January 2020): 116478. http://dx.doi.org/10.1016/j.energy.2019.116478.

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29

Jank, Wolfgang, and Shu Zhang. "An Automated and Data-Driven Bidding Strategy for Online Auctions." INFORMS Journal on Computing 23, no. 2 (2011): 238–53. http://dx.doi.org/10.1287/ijoc.1100.0397.

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30

Yang, Jidong, and Hejun Zhu. "A practical data-driven optimization strategy for Gaussian beam migration." GEOPHYSICS 83, no. 1 (2018): S81—S92. http://dx.doi.org/10.1190/geo2017-0314.1.

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Gaussian beam migration (GBM) is an efficient and accurate depth imaging technique, which allows us to resolve steep-dip structures and image multiple arrivals. Similar to Kirchhoff migration, GBM projects reflection events into the subsurface along traveltime isochrons. For data with low folds or signal-to-noise ratio (S/N), it produces migration artifacts, making it difficult for subsequent interpretation and attribute analysis. We have developed a data-driven optimization strategy to solve this problem. First, at the source and beam center locations, we estimate the instantaneous emergence

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31

Dong, Xiaonan, Clifford Goertemiller, Anuj Pal, Zongxuan Sun, Kenneth Kim, and Chol-Bum Mike Kweon. "Data Driven Feedforward Control Strategy for Multi-Fuel UAS Engine." IFAC-PapersOnLine 55, no. 37 (2022): 627–32. http://dx.doi.org/10.1016/j.ifacol.2022.11.252.

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32

Zhu, Guang, Xiaohong Chen, Jingye Li, and Kangkang Guo. "Data-Driven Seismic Impedance Inversion Based on Multi-Scale Strategy." Remote Sensing 14, no. 23 (2022): 6056. http://dx.doi.org/10.3390/rs14236056.

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Seismic impedance inversion is one of the most commonly used techniques for reservoir characterization. High accuracy and high resolution seismic impedance is a prerequisite for subsequent reservoir interpretation. The data-driven approach offers the opportunity for accurate impedance prediction by establishing a nonlinear mapping between seismic data and impedance. However, existing data-driven methods take the raw seismic data directly as input, making it difficult for the network to learn high frequency weak signal information and resulting in low resolution inversion results. In order to m

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Ma, Chong, and Haozheng Zhang. "A data-driven UUV decision-making test strategy design method." Journal of Physics: Conference Series 2674, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2674/1/012011.

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Abstract Based on the analysis of UUV control architecture and workflow, a data-driven test strategy design method was proposed to realize comprehensive online inspection of UUV, aiming at the requirements of full-process and full-function commissioning, system-level fault testing, single-step action of the actuator, software boundary testing, emergency control and fault-tolerant function verification of a certain UUV. At the same time, three types of typical faults of software, sensors and actuators are identified, and pseudo-random fault codes are injected into UUV control flow or informatio

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34

Intelligence and Neuroscience, Computational. "Retracted: A Data-Driven Control Strategy for Urban Express Ramp." Computational Intelligence and Neuroscience 2023 (November 1, 2023): 1. http://dx.doi.org/10.1155/2023/9761013.

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35

Velasco, D., L. Guzman, B. Puruncajas, C. Tutiven, and Y. Vidal. "Wind turbine blade damage detection using data-driven techniques." Renewable Energy and Power Quality Journal 21, no. 1 (2023): 462–66. http://dx.doi.org/10.24084/repqj21.357.

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This work presents a simple damage detection strategy for wind turbine blades. In particular, a vibration analysis-based damage detection methodology is proposed that requires only healthy data and detects damage in different locations of the blade. The stated structural health monitoring strategy is based on the extraction of characteristics using statistical metrics as a technique for the recognition and differentiation of healthy test experiments from damaged test experiments with simulated faults created by added mass. In this manner, several metrics are approached to find those that show

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Sayfullayevich, Mansurov Abdulla. "SOCIAL MANAGEMENT STRATEGY." European Journal of Learning on History and Social Sciences 1, no. 9 (2024): 1–4. https://doi.org/10.61796/ejlhss.v1i9.920.

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The article explores the concept of social management strategy, which involves structured approaches to addressing social challenges and promoting social welfare. It highlights the essential components of these strategies, including vision setting, stakeholder engagement, policy development, resource allocation, and community participation. The article also differentiates between government-led, corporate (CSR), and NGO-driven strategies, discussing their unique roles in fostering societal improvement. Furthermore, the challenges of implementing social management strategies—such as resource li

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Chen, Chuang, Cunsong Wang, Ningyun Lu, Bin Jiang, and Yin Xing. "A data-driven predictive maintenance strategy based on accurate failure prognostics." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 2 (2021): 387–94. http://dx.doi.org/10.17531/ein.2021.2.19.

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Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluat

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Satpathy, Rudra Bhanu, and Siddth Kumar Chhajer. "Collaborative Fuzzy Clustering Mechanism & Data Driven Fuzzy Neural System." Computational Intelligence and Machine Learning 1, no. 1 (2020): 1–13. http://dx.doi.org/10.36647/ciml/01.01.a001.

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In this Research, Fuzzy Logic guideline move system for self-developing Fuzzy neural deduction systems is anticipated. Highlights of proposed strategy, named (CFCM-DDNFS)Collaborative Fuzzy Clustering Mechanism & Data Driven Fuzzy Neural System mechanismwere; (1) Fuzzy guidelines are produced simply by Fuzzy c-means (FCM) and afterward adjusted by the (PCFC)preprocessed synergistic Fuzzy clustering method, and (2) Boundary & Structured learning are accomplished at the same time without choosing the underlying boundaries. The CFCM-DDNFS could implemented to manage enormous information i

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Sungheetha, Akey. "COVID-19 Risk Minimization Decision Making Strategy Using Data-Driven Model." March 2021 3, no. 1 (2021): 57–66. http://dx.doi.org/10.36548/jitdw.2021.1.006.

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In order to establish social resilient and sustainable cities during the pandemic outbreak, it is essential to forecast the epidemic trends and trace infection by means of data-driven solution addressing the requirements of local operational defense applications and global strategies. The smartphone based Digital Proximity Tracing Technology (DPTT) has obtained a great deal of interest with the ongoing COVID-19 pandemic in terms of mitigation, containing and monitoring with the population acceptance insights and effectiveness of the function. The DPTTs and Data-Driven Epidemic Intelligence Str

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Carpenter, Chris. "Data-Driven Management Strategy Can Reduce Environmental Effect of Production Plants." Journal of Petroleum Technology 71, no. 08 (2019): 74–75. http://dx.doi.org/10.2118/0819-0074-jpt.

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He, Bingjie, Jinxiu Xiao, and Qiaorong Dai. "Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid." Journal of Mathematics 2021 (September 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/9373204.

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The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ bo

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Feng, Rui, Baozhen Yao, Xiaoning Gu, Mingheng Zhang, and Bin Yu. "Environmental benefits mining based on data-driven taxi cruising recommendation strategy." Journal of Cleaner Production 326 (December 2021): 129376. http://dx.doi.org/10.1016/j.jclepro.2021.129376.

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Li, Yanmin, Chao Meng, Jintao Tian, Zhengyang Fang, and Huimin Cao. "Data-Driven Customer Online Shopping Behavior Analysis and Personalized Marketing Strategy." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–22. http://dx.doi.org/10.4018/joeuc.346230.

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In today's highly competitive market environment, personalized marketing has become an important means for enterprises to gain competitive advantages. In order to better meet customer needs, companies need to accurately identify and classify customers to implement more refined market strategies. This study focuses on the customer classification problem. Based on several classic deep learning models, the BiLSTM-TabNet model is designed, and the Whale Optimization Algorithm (WOA) is introduced to further improve the model performance, thereby improving classification accuracy and practicality. E

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Feng, Xiaodong, Chengwei Li, Qi Cai, Shirong Huang, and Yiyi Zhou. "Data driven strategy on structural optimization using beetle-genetic hybrid algorithm." Structures 73 (March 2025): 108314. https://doi.org/10.1016/j.istruc.2025.108314.

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Yu, Hao, Qingquan Jia, Haidong Sun, and Haiyan Dong. "Data-driven partition control strategy for harmonic distortion and voltage unbalance." IET Generation, Transmission & Distribution 12, no. 9 (2018): 2038–45. http://dx.doi.org/10.1049/iet-gtd.2017.1297.

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Bellgard, Matthew I. "ERDMAS: An exemplar-driven institutional research data management and analysis strategy." International Journal of Information Management 50 (February 2020): 337–40. http://dx.doi.org/10.1016/j.ijinfomgt.2019.08.009.

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47

Ferri, Gabriele, Andrea Munafo, and Kevin D. LePage. "An Autonomous Underwater Vehicle Data-Driven Control Strategy for Target Tracking." IEEE Journal of Oceanic Engineering 43, no. 2 (2018): 323–43. http://dx.doi.org/10.1109/joe.2018.2797558.

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Ragazzo, Carlos Emmanuel Joppert, and Guilherme Almeida. "A Data-Driven Management Training Strategy." Educação & Realidade 47 (2022). http://dx.doi.org/10.1590/2175-6236116628vs02.

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ABSTRACT In this paper, we describe a proposal to address the problem of equity within large municipal school networks. A principal training program that uses a peer mentoring strategy is believed to be able to improve the performance of schools that consistently perform the worst. To make the case for the proposal, we discuss international experiences, recent work that identifies peers from geographically close schools that nonetheless perform very differently and report the results of novel empirical analyses that reveal systematic differences between principals of schools performing well an

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49

Berumen, Alfonso, and Chad Cavanaugh. "Developing a KPI-Driven Data Strategy." SSRN Electronic Journal, 2025. https://doi.org/10.2139/ssrn.5222562.

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50

"Data-driven decisions." Strategic Direction ahead-of-print, ahead-of-print (2021). http://dx.doi.org/10.1108/sd-06-2021-0060.

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Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Social media data has the ability to drastically improve the strategic outlooks and planning of SMEs in the hospitality sector, is sufficient investment is made in the collection and analysis of this data. Originality The briefing saves busy executives, strategists and researchers hours of r

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